8 citations · 17 across the 4 of their papers we have counts for
4 papers
Rician likelihood loss for quantitative MRI using self-supervised deep learning
Christopher S. Parker, Anna Schroder, Sean C. Epstein +3
Purpose: Previous quantitative MR imaging studies using self-supervised deep learning have reported biased parameter estimates at low SNR. Such systematic errors arise from the cho…
Resolving quantitative MRI model degeneracy with machine learning via training data distribution design
Michele Guerreri, Sean Epstein, Hojjat Azadbakht +1
Quantitative MRI (qMRI) aims to map tissue properties non-invasively via models that relate these unknown quantities to measured MRI signals. Estimating these unknowns, which has t…
Fitting a Directional Microstructure Model to Diffusion-Relaxation MRI Data with Self-Supervised Machine Learning
Jason P. Lim, Stefano B. Blumberg, Neil Narayan +4
Machine learning is a powerful approach for fitting microstructural models to diffusion MRI data. Early machine learning microstructure imaging implementations trained regressors t…
Choice of training label matters: how to best use deep learning for quantitative MRI parameter estimation
Sean C. Epstein, Timothy J. P. Bray, Margaret Hall-Craggs +1
Deep learning (DL) is gaining popularity as a parameter estimation method for quantitative MRI. A range of competing implementations have been proposed, relying on either supervise…